Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations21,428
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 MiB
Average record size in memory355.4 B

Variable types

Categorical8
Numeric10

Alerts

market_segment_type is highly overall correlated with repeated_guestHigh correlation
no_of_previous_bookings_not_canceled is highly overall correlated with repeated_guestHigh correlation
repeated_guest is highly overall correlated with market_segment_type and 1 other fieldsHigh correlation
no_of_adults is highly imbalanced (51.5%) Imbalance
type_of_meal_plan is highly imbalanced (54.0%) Imbalance
required_car_parking_space is highly imbalanced (74.8%) Imbalance
room_type_reserved is highly imbalanced (56.7%) Imbalance
market_segment_type is highly imbalanced (54.4%) Imbalance
repeated_guest is highly imbalanced (79.7%) Imbalance
no_of_previous_cancellations is highly skewed (γ1 = 21.9574157) Skewed
no_of_children has 19304 (90.1%) zeros Zeros
no_of_weekend_nights has 9171 (42.8%) zeros Zeros
no_of_week_nights has 1497 (7.0%) zeros Zeros
lead_time has 944 (4.4%) zeros Zeros
no_of_previous_cancellations has 21221 (99.0%) zeros Zeros
no_of_previous_bookings_not_canceled has 20795 (97.0%) zeros Zeros
avg_price_per_room has 368 (1.7%) zeros Zeros
no_of_special_requests has 9925 (46.3%) zeros Zeros

Reproduction

Analysis started2025-05-11 06:44:01.884740
Analysis finished2025-05-11 06:44:15.744256
Duration13.86 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

no_of_adults
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2
15596 
1
3950 
3
1751 
0
 
117
4
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21,428
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 15596
72.8%
1 3950
 
18.4%
3 1751
 
8.2%
0 117
 
0.5%
4 14
 
0.1%

Length

2025-05-11T12:14:15.829575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:15.971107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 15596
72.8%
1 3950
 
18.4%
3 1751
 
8.2%
0 117
 
0.5%
4 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 15596
72.8%
1 3950
 
18.4%
3 1751
 
8.2%
0 117
 
0.5%
4 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15596
72.8%
1 3950
 
18.4%
3 1751
 
8.2%
0 117
 
0.5%
4 14
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15596
72.8%
1 3950
 
18.4%
3 1751
 
8.2%
0 117
 
0.5%
4 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15596
72.8%
1 3950
 
18.4%
3 1751
 
8.2%
0 117
 
0.5%
4 14
 
0.1%

no_of_children
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1413571
Minimum0
Maximum10
Zeros19304
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:16.082441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46468757
Coefficient of variation (CV)3.2873309
Kurtosis30.682261
Mean0.1413571
Median Absolute Deviation (MAD)0
Skewness4.1437565
Sum3029
Variance0.21593454
MonotonicityNot monotonic
2025-05-11T12:14:16.186100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19304
90.1%
1 1257
 
5.9%
2 848
 
4.0%
3 16
 
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 19304
90.1%
1 1257
 
5.9%
2 848
 
4.0%
3 16
 
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 2
 
< 0.1%
3 16
 
0.1%
2 848
 
4.0%
1 1257
 
5.9%
0 19304
90.1%

no_of_weekend_nights
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87913011
Minimum0
Maximum6
Zeros9171
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:16.283741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.88370138
Coefficient of variation (CV)1.0051998
Kurtosis0.1472271
Mean0.87913011
Median Absolute Deviation (MAD)1
Skewness0.62570335
Sum18838
Variance0.78092813
MonotonicityNot monotonic
2025-05-11T12:14:16.384157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 9171
42.8%
1 6090
28.4%
2 5925
27.7%
3 116
 
0.5%
4 95
 
0.4%
5 16
 
0.1%
6 15
 
0.1%
ValueCountFrequency (%)
0 9171
42.8%
1 6090
28.4%
2 5925
27.7%
3 116
 
0.5%
4 95
 
0.4%
5 16
 
0.1%
6 15
 
0.1%
ValueCountFrequency (%)
6 15
 
0.1%
5 16
 
0.1%
4 95
 
0.4%
3 116
 
0.5%
2 5925
27.7%
1 6090
28.4%
0 9171
42.8%

no_of_week_nights
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2556468
Minimum0
Maximum17
Zeros1497
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:16.488429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4942457
Coefficient of variation (CV)0.66244666
Kurtosis6.3394119
Mean2.2556468
Median Absolute Deviation (MAD)1
Skewness1.488749
Sum48334
Variance2.2327702
MonotonicityNot monotonic
2025-05-11T12:14:16.605463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 5967
27.8%
1 5817
27.1%
3 4600
21.5%
4 1997
 
9.3%
0 1497
 
7.0%
5 1165
 
5.4%
6 147
 
0.7%
7 88
 
0.4%
8 53
 
0.2%
10 34
 
0.2%
Other values (7) 63
 
0.3%
ValueCountFrequency (%)
0 1497
 
7.0%
1 5817
27.1%
2 5967
27.8%
3 4600
21.5%
4 1997
 
9.3%
5 1165
 
5.4%
6 147
 
0.7%
7 88
 
0.4%
8 53
 
0.2%
9 27
 
0.1%
ValueCountFrequency (%)
17 2
 
< 0.1%
15 6
 
< 0.1%
14 6
 
< 0.1%
13 5
 
< 0.1%
12 5
 
< 0.1%
11 12
 
0.1%
10 34
 
0.2%
9 27
 
0.1%
8 53
0.2%
7 88
0.4%

type_of_meal_plan
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Meal Plan 1
16754 
Not Selected
3692 
Meal Plan 2
 
978
Meal Plan 3
 
4

Length

Max length12
Median length11
Mean length11.172298
Min length11

Characters and Unicode

Total characters2,39,400
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Selected
2nd rowMeal Plan 1
3rd rowMeal Plan 1
4th rowNot Selected
5th rowMeal Plan 1

Common Values

ValueCountFrequency (%)
Meal Plan 1 16754
78.2%
Not Selected 3692
 
17.2%
Meal Plan 2 978
 
4.6%
Meal Plan 3 4
 
< 0.1%

Length

2025-05-11T12:14:16.735158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:16.851976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
meal 17736
29.3%
plan 17736
29.3%
1 16754
27.7%
not 3692
 
6.1%
selected 3692
 
6.1%
2 978
 
1.6%
3 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 39164
16.4%
39164
16.4%
a 35472
14.8%
e 28812
12.0%
M 17736
7.4%
P 17736
7.4%
n 17736
7.4%
1 16754
7.0%
t 7384
 
3.1%
N 3692
 
1.5%
Other values (6) 15750
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139644
58.3%
Uppercase Letter 42856
 
17.9%
Space Separator 39164
 
16.4%
Decimal Number 17736
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 39164
28.0%
a 35472
25.4%
e 28812
20.6%
n 17736
12.7%
t 7384
 
5.3%
o 3692
 
2.6%
c 3692
 
2.6%
d 3692
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
M 17736
41.4%
P 17736
41.4%
N 3692
 
8.6%
S 3692
 
8.6%
Decimal Number
ValueCountFrequency (%)
1 16754
94.5%
2 978
 
5.5%
3 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
39164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 182500
76.2%
Common 56900
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 39164
21.5%
a 35472
19.4%
e 28812
15.8%
M 17736
9.7%
P 17736
9.7%
n 17736
9.7%
t 7384
 
4.0%
N 3692
 
2.0%
o 3692
 
2.0%
S 3692
 
2.0%
Other values (2) 7384
 
4.0%
Common
ValueCountFrequency (%)
39164
68.8%
1 16754
29.4%
2 978
 
1.7%
3 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 39164
16.4%
39164
16.4%
a 35472
14.8%
e 28812
12.0%
M 17736
7.4%
P 17736
7.4%
n 17736
7.4%
1 16754
7.0%
t 7384
 
3.1%
N 3692
 
1.5%
Other values (6) 15750
6.6%

required_car_parking_space
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
20527 
1
 
901

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21,428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20527
95.8%
1 901
 
4.2%

Length

2025-05-11T12:14:17.001632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:17.110801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20527
95.8%
1 901
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 20527
95.8%
1 901
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20527
95.8%
1 901
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20527
95.8%
1 901
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20527
95.8%
1 901
 
4.2%

room_type_reserved
Categorical

Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Room_Type 1
15451 
Room_Type 4
4378 
Room_Type 6
 
777
Room_Type 2
 
506
Room_Type 5
 
189
Other values (2)
 
127

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2,35,708
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoom_Type 1
2nd rowRoom_Type 1
3rd rowRoom_Type 1
4th rowRoom_Type 1
5th rowRoom_Type 1

Common Values

ValueCountFrequency (%)
Room_Type 1 15451
72.1%
Room_Type 4 4378
 
20.4%
Room_Type 6 777
 
3.6%
Room_Type 2 506
 
2.4%
Room_Type 5 189
 
0.9%
Room_Type 7 122
 
0.6%
Room_Type 3 5
 
< 0.1%

Length

2025-05-11T12:14:17.216297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:17.332251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
room_type 21428
50.0%
1 15451
36.1%
4 4378
 
10.2%
6 777
 
1.8%
2 506
 
1.2%
5 189
 
0.4%
7 122
 
0.3%
3 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 42856
18.2%
R 21428
9.1%
m 21428
9.1%
_ 21428
9.1%
T 21428
9.1%
y 21428
9.1%
p 21428
9.1%
e 21428
9.1%
21428
9.1%
1 15451
 
6.6%
Other values (6) 5977
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128568
54.5%
Uppercase Letter 42856
 
18.2%
Connector Punctuation 21428
 
9.1%
Space Separator 21428
 
9.1%
Decimal Number 21428
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15451
72.1%
4 4378
 
20.4%
6 777
 
3.6%
2 506
 
2.4%
5 189
 
0.9%
7 122
 
0.6%
3 5
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 42856
33.3%
m 21428
16.7%
y 21428
16.7%
p 21428
16.7%
e 21428
16.7%
Uppercase Letter
ValueCountFrequency (%)
R 21428
50.0%
T 21428
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 21428
100.0%
Space Separator
ValueCountFrequency (%)
21428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 171424
72.7%
Common 64284
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 21428
33.3%
21428
33.3%
1 15451
24.0%
4 4378
 
6.8%
6 777
 
1.2%
2 506
 
0.8%
5 189
 
0.3%
7 122
 
0.2%
3 5
 
< 0.1%
Latin
ValueCountFrequency (%)
o 42856
25.0%
R 21428
12.5%
m 21428
12.5%
T 21428
12.5%
y 21428
12.5%
p 21428
12.5%
e 21428
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 42856
18.2%
R 21428
9.1%
m 21428
9.1%
_ 21428
9.1%
T 21428
9.1%
y 21428
9.1%
p 21428
9.1%
e 21428
9.1%
21428
9.1%
1 15451
 
6.6%
Other values (6) 5977
 
2.5%

lead_time
Real number (ℝ)

Zeros 

Distinct351
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.349776
Minimum0
Maximum443
Zeros944
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:17.478616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median45
Q3101.25
95-th percentile210
Maximum443
Range443
Interquartile range (IQR)89.25

Descriptive statistics

Standard deviation69.447828
Coefficient of variation (CV)1.0311516
Kurtosis1.8205715
Mean67.349776
Median Absolute Deviation (MAD)38
Skewness1.39884
Sum1443171
Variance4823.0009
MonotonicityNot monotonic
2025-05-11T12:14:17.617508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 944
 
4.4%
1 733
 
3.4%
2 484
 
2.3%
4 453
 
2.1%
3 451
 
2.1%
5 404
 
1.9%
6 360
 
1.7%
7 314
 
1.5%
8 297
 
1.4%
12 259
 
1.2%
Other values (341) 16729
78.1%
ValueCountFrequency (%)
0 944
4.4%
1 733
3.4%
2 484
2.3%
3 451
2.1%
4 453
2.1%
5 404
1.9%
6 360
 
1.7%
7 314
 
1.5%
8 297
 
1.4%
9 248
 
1.2%
ValueCountFrequency (%)
443 2
 
< 0.1%
433 2
 
< 0.1%
418 5
< 0.1%
386 8
< 0.1%
381 2
 
< 0.1%
377 6
< 0.1%
372 1
 
< 0.1%
361 1
 
< 0.1%
359 6
< 0.1%
355 1
 
< 0.1%

arrival_year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2018
18230 
2017
3198 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters85,712
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 18230
85.1%
2017 3198
 
14.9%

Length

2025-05-11T12:14:17.738159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:17.838916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 18230
85.1%
2017 3198
 
14.9%

Most occurring characters

ValueCountFrequency (%)
2 21428
25.0%
0 21428
25.0%
1 21428
25.0%
8 18230
21.3%
7 3198
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 85712
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 21428
25.0%
0 21428
25.0%
1 21428
25.0%
8 18230
21.3%
7 3198
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 85712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 21428
25.0%
0 21428
25.0%
1 21428
25.0%
8 18230
21.3%
7 3198
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 21428
25.0%
0 21428
25.0%
1 21428
25.0%
8 18230
21.3%
7 3198
 
3.7%

arrival_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.337269
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:17.938535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1441285
Coefficient of variation (CV)0.42851482
Kurtosis-1.0026178
Mean7.337269
Median Absolute Deviation (MAD)2
Skewness-0.29729955
Sum157223
Variance9.8855443
MonotonicityNot monotonic
2025-05-11T12:14:18.048756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 2646
12.3%
8 2542
11.9%
9 2518
11.8%
12 1971
9.2%
7 1850
8.6%
11 1823
8.5%
4 1668
7.8%
3 1646
7.7%
6 1529
7.1%
5 1504
7.0%
Other values (2) 1731
8.1%
ValueCountFrequency (%)
1 656
 
3.1%
2 1075
5.0%
3 1646
7.7%
4 1668
7.8%
5 1504
7.0%
6 1529
7.1%
7 1850
8.6%
8 2542
11.9%
9 2518
11.8%
10 2646
12.3%
ValueCountFrequency (%)
12 1971
9.2%
11 1823
8.5%
10 2646
12.3%
9 2518
11.8%
8 2542
11.9%
7 1850
8.6%
6 1529
7.1%
5 1504
7.0%
4 1668
7.8%
3 1646
7.7%

arrival_date
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.74202
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:18.163586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8433257
Coefficient of variation (CV)0.56176563
Kurtosis-1.2045586
Mean15.74202
Median Absolute Deviation (MAD)8
Skewness0.015919099
Sum337320
Variance78.20441
MonotonicityNot monotonic
2025-05-11T12:14:18.288158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
26 784
 
3.7%
2 774
 
3.6%
7 769
 
3.6%
4 756
 
3.5%
19 756
 
3.5%
20 746
 
3.5%
8 743
 
3.5%
29 743
 
3.5%
17 730
 
3.4%
11 728
 
3.4%
Other values (21) 13899
64.9%
ValueCountFrequency (%)
1 647
3.0%
2 774
3.6%
3 669
3.1%
4 756
3.5%
5 699
3.3%
6 668
3.1%
7 769
3.6%
8 743
3.5%
9 688
3.2%
10 645
3.0%
ValueCountFrequency (%)
31 400
1.9%
30 676
3.2%
29 743
3.5%
28 718
3.4%
27 693
3.2%
26 784
3.7%
25 679
3.2%
24 590
2.8%
23 615
2.9%
22 662
3.1%

market_segment_type
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Online
16432 
Offline
3471 
Corporate
 
1169
Complementary
 
274
Aviation
 
82

Length

Max length13
Median length6
Mean length6.4228113
Min length6

Characters and Unicode

Total characters1,37,628
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline
2nd rowOnline
3rd rowOffline
4th rowOnline
5th rowOnline

Common Values

ValueCountFrequency (%)
Online 16432
76.7%
Offline 3471
 
16.2%
Corporate 1169
 
5.5%
Complementary 274
 
1.3%
Aviation 82
 
0.4%

Length

2025-05-11T12:14:18.422050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:18.541323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
online 16432
76.7%
offline 3471
 
16.2%
corporate 1169
 
5.5%
complementary 274
 
1.3%
aviation 82
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 36691
26.7%
e 21620
15.7%
l 20177
14.7%
i 20067
14.6%
O 19903
14.5%
f 6942
 
5.0%
o 2694
 
2.0%
r 2612
 
1.9%
a 1525
 
1.1%
t 1525
 
1.1%
Other values (6) 3872
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116200
84.4%
Uppercase Letter 21428
 
15.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 36691
31.6%
e 21620
18.6%
l 20177
17.4%
i 20067
17.3%
f 6942
 
6.0%
o 2694
 
2.3%
r 2612
 
2.2%
a 1525
 
1.3%
t 1525
 
1.3%
p 1443
 
1.2%
Other values (3) 904
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
O 19903
92.9%
C 1443
 
6.7%
A 82
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 137628
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 36691
26.7%
e 21620
15.7%
l 20177
14.7%
i 20067
14.6%
O 19903
14.5%
f 6942
 
5.0%
o 2694
 
2.0%
r 2612
 
1.9%
a 1525
 
1.1%
t 1525
 
1.1%
Other values (6) 3872
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 36691
26.7%
e 21620
15.7%
l 20177
14.7%
i 20067
14.6%
O 19903
14.5%
f 6942
 
5.0%
o 2694
 
2.0%
r 2612
 
1.9%
a 1525
 
1.1%
t 1525
 
1.1%
Other values (6) 3872
 
2.8%

repeated_guest
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
20750 
1
 
678

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21,428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20750
96.8%
1 678
 
3.2%

Length

2025-05-11T12:14:18.660983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:18.760799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20750
96.8%
1 678
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 20750
96.8%
1 678
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21428
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20750
96.8%
1 678
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20750
96.8%
1 678
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20750
96.8%
1 678
 
3.2%

no_of_previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028467426
Minimum0
Maximum13
Zeros21221
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:18.854552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42699297
Coefficient of variation (CV)14.999353
Kurtosis540.07914
Mean0.028467426
Median Absolute Deviation (MAD)0
Skewness21.957416
Sum610
Variance0.182323
MonotonicityNot monotonic
2025-05-11T12:14:18.966256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 21221
99.0%
1 103
 
0.5%
2 33
 
0.2%
3 29
 
0.1%
11 24
 
0.1%
4 9
 
< 0.1%
5 7
 
< 0.1%
13 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 21221
99.0%
1 103
 
0.5%
2 33
 
0.2%
3 29
 
0.1%
4 9
 
< 0.1%
5 7
 
< 0.1%
6 1
 
< 0.1%
11 24
 
0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 24
 
0.1%
6 1
 
< 0.1%
5 7
 
< 0.1%
4 9
 
< 0.1%
3 29
 
0.1%
2 33
 
0.2%
1 103
 
0.5%
0 21221
99.0%

no_of_previous_bookings_not_canceled
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19801195
Minimum0
Maximum58
Zeros20795
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:19.091841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9586765
Coefficient of variation (CV)9.8917087
Kurtosis345.7301
Mean0.19801195
Median Absolute Deviation (MAD)0
Skewness16.704832
Sum4243
Variance3.8364137
MonotonicityNot monotonic
2025-05-11T12:14:19.234146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20795
97.0%
1 181
 
0.8%
2 81
 
0.4%
3 61
 
0.3%
4 55
 
0.3%
5 50
 
0.2%
6 31
 
0.1%
8 19
 
0.1%
7 19
 
0.1%
11 14
 
0.1%
Other values (41) 122
 
0.6%
ValueCountFrequency (%)
0 20795
97.0%
1 181
 
0.8%
2 81
 
0.4%
3 61
 
0.3%
4 55
 
0.3%
5 50
 
0.2%
6 31
 
0.1%
7 19
 
0.1%
8 19
 
0.1%
9 14
 
0.1%
ValueCountFrequency (%)
58 1
< 0.1%
56 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%
48 2
< 0.1%
47 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%

avg_price_per_room
Real number (ℝ)

Zeros 

Distinct3519
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.52101
Minimum0
Maximum365
Zeros368
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:19.374177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q180.75
median99.9
Q3126.9
95-th percentile170.33
Maximum365
Range365
Interquartile range (IQR)46.15

Descriptive statistics

Standard deviation37.593969
Coefficient of variation (CV)0.35626997
Kurtosis2.0145411
Mean105.52101
Median Absolute Deviation (MAD)21.6
Skewness0.56285404
Sum2261104.3
Variance1413.3065
MonotonicityNot monotonic
2025-05-11T12:14:19.518340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 434
 
2.0%
0 368
 
1.7%
75 343
 
1.6%
95 276
 
1.3%
85 271
 
1.3%
90 253
 
1.2%
80.75 239
 
1.1%
94.5 221
 
1.0%
96.3 211
 
1.0%
76.5 195
 
0.9%
Other values (3509) 18617
86.9%
ValueCountFrequency (%)
0 368
1.7%
0.5 1
 
< 0.1%
1 6
 
< 0.1%
1.48 1
 
< 0.1%
1.6 1
 
< 0.1%
2 5
 
< 0.1%
3 2
 
< 0.1%
4.5 1
 
< 0.1%
6 17
 
0.1%
6.5 1
 
< 0.1%
ValueCountFrequency (%)
365 1
 
< 0.1%
349.63 1
 
< 0.1%
332.57 1
 
< 0.1%
316 1
 
< 0.1%
314.1 1
 
< 0.1%
306 2
< 0.1%
300 4
< 0.1%
299.33 1
 
< 0.1%
297 1
 
< 0.1%
296 1
 
< 0.1%

no_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73819302
Minimum0
Maximum5
Zeros9925
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size334.8 KiB
2025-05-11T12:14:19.638584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81238784
Coefficient of variation (CV)1.1005087
Kurtosis0.39371347
Mean0.73819302
Median Absolute Deviation (MAD)1
Skewness0.90896341
Sum15818
Variance0.659974
MonotonicityNot monotonic
2025-05-11T12:14:19.748917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9925
46.3%
1 7803
36.4%
2 3150
 
14.7%
3 490
 
2.3%
4 55
 
0.3%
5 5
 
< 0.1%
ValueCountFrequency (%)
0 9925
46.3%
1 7803
36.4%
2 3150
 
14.7%
3 490
 
2.3%
4 55
 
0.3%
5 5
 
< 0.1%
ValueCountFrequency (%)
5 5
 
< 0.1%
4 55
 
0.3%
3 490
 
2.3%
2 3150
 
14.7%
1 7803
36.4%
0 9925
46.3%

booking_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Not_Canceled
15282 
Canceled
6146 

Length

Max length12
Median length12
Mean length10.852716
Min length8

Characters and Unicode

Total characters2,32,552
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot_Canceled
2nd rowCanceled
3rd rowNot_Canceled
4th rowNot_Canceled
5th rowCanceled

Common Values

ValueCountFrequency (%)
Not_Canceled 15282
71.3%
Canceled 6146
28.7%

Length

2025-05-11T12:14:19.883255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T12:14:19.997864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
not_canceled 15282
71.3%
canceled 6146
28.7%

Most occurring characters

ValueCountFrequency (%)
e 42856
18.4%
C 21428
9.2%
a 21428
9.2%
n 21428
9.2%
c 21428
9.2%
l 21428
9.2%
d 21428
9.2%
N 15282
 
6.6%
o 15282
 
6.6%
t 15282
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 180560
77.6%
Uppercase Letter 36710
 
15.8%
Connector Punctuation 15282
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 42856
23.7%
a 21428
11.9%
n 21428
11.9%
c 21428
11.9%
l 21428
11.9%
d 21428
11.9%
o 15282
 
8.5%
t 15282
 
8.5%
Uppercase Letter
ValueCountFrequency (%)
C 21428
58.4%
N 15282
41.6%
Connector Punctuation
ValueCountFrequency (%)
_ 15282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 217270
93.4%
Common 15282
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 42856
19.7%
C 21428
9.9%
a 21428
9.9%
n 21428
9.9%
c 21428
9.9%
l 21428
9.9%
d 21428
9.9%
N 15282
 
7.0%
o 15282
 
7.0%
t 15282
 
7.0%
Common
ValueCountFrequency (%)
_ 15282
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 42856
18.4%
C 21428
9.2%
a 21428
9.2%
n 21428
9.2%
c 21428
9.2%
l 21428
9.2%
d 21428
9.2%
N 15282
 
6.6%
o 15282
 
6.6%
t 15282
 
6.6%

Interactions

2025-05-11T12:14:13.963594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.156053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.223943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.273208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.393533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.373525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.656049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.729319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.807370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.896234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.060398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.284687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.323856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.384720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.488284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.469632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.755639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.830565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.911334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.999715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.160018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.388785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.427252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.508341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.587020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.575919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.860073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.941323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.025054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.111292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.279285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.494431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.536375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.618283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.690706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.681844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.975482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.057805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.136491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.226936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.412741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.590014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.637749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.719450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.780057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.784423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.071819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.160276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.237100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.330566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.514179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.689077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.738914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.823936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.877192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.892544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.175353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.264854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.347900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.433797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.617393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.793218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.842497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.932055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.974943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.232991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.294626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.374262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.465971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.538023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.725553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:04.897995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.945087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.039173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.074607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.350680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.408717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.479460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.574133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.644163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.837971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.010584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.050657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.159688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.174695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.453905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.516392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.588392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.684748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.748578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:14.944241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:05.122803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:06.171609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:07.272725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:08.278308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:09.558521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:10.626915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:11.700764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:12.794760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-05-11T12:14:13.858440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2025-05-11T12:14:20.092576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
arrival_datearrival_montharrival_yearavg_price_per_roombooking_statuslead_timemarket_segment_typeno_of_adultsno_of_childrenno_of_previous_bookings_not_canceledno_of_previous_cancellationsno_of_special_requestsno_of_week_nightsno_of_weekend_nightsrepeated_guestrequired_car_parking_spaceroom_type_reservedtype_of_meal_plan
arrival_date1.000-0.0230.0500.0140.0260.0240.0190.0290.025-0.006-0.012-0.001-0.0010.0060.0260.0000.0180.020
arrival_month-0.0231.0000.3650.0400.1780.0750.0710.085-0.002-0.003-0.0030.1190.0350.0140.0860.0620.0500.040
arrival_year0.0500.3651.0000.1920.1530.1710.1350.1130.0280.0140.0270.0400.0150.0370.0100.0000.0980.105
avg_price_per_room0.0140.0400.1921.0000.173-0.0010.4310.2080.268-0.203-0.1140.1980.015-0.0130.2790.0680.3050.146
booking_status0.0260.1780.1530.1731.0000.3710.2150.0970.0590.0580.0420.2670.1250.0870.1110.0870.0790.052
lead_time0.0240.0750.171-0.0010.3711.0000.1240.1190.028-0.211-0.1020.0160.2980.1770.1620.0440.0620.088
market_segment_type0.0190.0710.1350.4310.2150.1241.0000.2190.0530.1730.1120.1690.0970.0830.5480.1110.1410.158
no_of_adults0.0290.0850.1130.2080.0970.1190.2191.0000.1870.0850.0500.0970.0940.0680.2890.0120.3380.089
no_of_children0.025-0.0020.0280.2680.0590.0280.0530.1871.000-0.049-0.0330.1080.0110.0100.0330.0220.4030.046
no_of_previous_bookings_not_canceled-0.006-0.0030.014-0.2030.058-0.2110.1730.085-0.0491.0000.445-0.026-0.140-0.0900.5430.0500.0360.018
no_of_previous_cancellations-0.012-0.0030.027-0.1140.042-0.1020.1120.050-0.0330.4451.000-0.025-0.059-0.0370.3860.0160.0460.012
no_of_special_requests-0.0010.1190.0400.1980.2670.0160.1690.0970.108-0.026-0.0251.0000.0430.0150.0550.0780.0590.036
no_of_week_nights-0.0010.0350.0150.0150.1250.2980.0970.0940.011-0.140-0.0590.0431.0000.0760.1400.0590.0480.053
no_of_weekend_nights0.0060.0140.037-0.0130.0870.1770.0830.0680.010-0.090-0.0370.0150.0761.0000.0880.0540.0230.034
repeated_guest0.0260.0860.0100.2790.1110.1620.5480.2890.0330.5430.3860.0550.1400.0881.0000.1030.0840.074
required_car_parking_space0.0000.0620.0000.0680.0870.0440.1110.0120.0220.0500.0160.0780.0590.0540.1031.0000.0300.029
room_type_reserved0.0180.0500.0980.3050.0790.0620.1410.3380.4030.0360.0460.0590.0480.0230.0840.0301.0000.163
type_of_meal_plan0.0200.0400.1050.1460.0520.0880.1580.0890.0460.0180.0120.0360.0530.0340.0740.0290.1631.000

Missing values

2025-05-11T12:14:15.310214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-11T12:14:15.592902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

no_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
02002Not Selected0Room_Type 1122018122Online00075.000Not_Canceled
11021Meal Plan 10Room_Type 112018228Online00060.000Canceled
22014Meal Plan 10Room_Type 11412018713Offline00072.252Not_Canceled
33022Not Selected0Room_Type 11352018715Online00092.282Not_Canceled
42002Meal Plan 10Room_Type 12452018617Online00075.000Canceled
52126Meal Plan 10Room_Type 171201891Online000150.983Not_Canceled
63002Meal Plan 10Room_Type 4932018712Online000137.702Not_Canceled
72013Meal Plan 10Room_Type 1172018128Online000100.380Not_Canceled
82003Meal Plan 10Room_Type 450201891Online00098.640Not_Canceled
91011Meal Plan 20Room_Type 13012018730Offline00090.000Not_Canceled
no_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
290081011Meal Plan 10Room_Type 4112018103Online000130.901Not_Canceled
290091001Meal Plan 10Room_Type 10201823Online00079.000Not_Canceled
290122013Not Selected0Room_Type 1392018117Online000148.000Canceled
290132023Meal Plan 10Room_Type 1148201856Online00099.450Not_Canceled
290141101Meal Plan 10Room_Type 15201792Offline00060.001Not_Canceled
290152004Meal Plan 10Room_Type 519201792Offline00083.551Not_Canceled
290162023Meal Plan 10Room_Type 126201873Offline00085.000Not_Canceled
290172113Meal Plan 20Room_Type 1150201877Online000173.250Canceled
290182102Meal Plan 10Room_Type 112720181222Online000106.203Not_Canceled
290192001Not Selected0Room_Type 132018419Online00089.002Not_Canceled